A computational model for synaptic message transmission

TL;DR

This paper introduces a quantum-inspired computational model for synaptic message transmission, where neurotransmitters and receptors act as a decision tree to probabilistically excite or inhibit synapses, with learning via genetic programming.

Contribution

It presents a novel quantum-inspired framework for modeling synaptic transmission, integrating decision tree concepts and genetic programming for learning.

Findings

Model successfully simulates synaptic message patterns

Quantum decision tree effectively captures excitation and inhibition dynamics

Genetic programming enables adaptive learning of transmission sequences

Abstract

A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical "quantum decision tree" to "decide" whether to excite or inhibit the synapse. When a neurotransmitter binds to its corresponding receptor, it is the equivalent of randomly choosing different "strategies"; a "strategy" has two actions to take: excite or inhibit the synapse with a certain probability. The genetic programming can be applied for learning the observed data sequence to simulate the synaptic message transmission.